Road Condition and

Monitoring System

utilizing Motorcycle

motion

IR. DR. MUHAMMAD MARIZWAN BIN ABDUL MANAN

Intelligent Transport and System Development Unit (ITS)

Malaysian Institute of Road Safety Research (MIROS)

21 February 2017

marizwan@miros.gov.my

Motorcycle safety

• 24% of world traffic death occurs among motorcyclists 2

• On average, more than 70 motorcyclists are killed in road traffic crashes

every week, and more than 13 motorcyclists are killed every day in

Malaysia 1,2

2

Sources1. Abdul Manan, M. M., Jonsson, T., & Várhelyi, A. (2013). Safety Science, 60, 13-20.2. WHO. (2013). Global status report on road safety 2013: supporting a decade of action. In. Geneva, Switzerland: World Health Organization.

Motorcycle fatalities and economic loss for Malaysia roads

3

4,036

4,1694,178

4,294

4,179

4,203

2.56

2.742.85

3.03 3.083.20

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

3,900

3,950

4,000

4,050

4,100

4,150

4,200

4,250

4,300

4,350

2010 2011 2012 2013 2014 2015

Bil

lio

n U

SD

Mo

torc

ycl

e f

ata

liti

es

Motorcyclists fatality cases Economic loss based on Malaysian GDP per capita (Billion USD)

Source: PDRM 2012, JKJR, MIROS 2014, WHO 2014, BNM 2015)

3.2 Billion USD (RM14 Billion) loss

Malaysia could have provide

- 7,000km of exclusive motorcycle

lane or

- 14 million expensive full face

helmet and jackets or

- 200 thousand of motorcycle with

ABS

Research gapsBil Research gap area Problem statement Sources

1 Road condition Motorcycle safety is influence by the road condition:

rutting, corrugation, cracks, potholes, undulation

Lenkeit et al. 2011, Forkenbrock et al.,

Elliott et al. 2003, Haque et al. 2009, ,

Vittorio et al, 2016, Haworth 2012,

Abdul Manan et al. 2015

2 Road maintenance Road maintenance activities have to be done

systematically and cost-effectively by the road authorities

due to lack of funds.

Eriksson et al. 2008, PWD 2009,

3 Intelligent transportation

system

The current developed ITS / geospatial application for road

defect detection; services and applications are usually

designed for four-wheeled vehicles and rarely for

motorcycles.

Vittorio et al. (2014), Vittorio et al.

(2012), Eriksson et al. (2008) and

Alessandroni et al. (2014), Kitani 2013

4

Why motorcycles?• Behaviour of motorcycle is different from that of car (Kitani et al. 2012)

– A motorcycle is not able to be self-standing,

– A rider should always keep the motorcycle’s balance to avoid a road crash

– A motorcycle leans its body to turn into a corner – counter balance to the centrifugal force

• It is with this criteria (i.e. need balancing and complex handling) motorcycles have the advantage (over a 4-

wheeled vehicle) to move horizontally on the road lane and with its instability will make the motorcycle

more sensitive to the road surface condition and quality (Lenkeit et al. 2011, Kitani 2013).

• Motorcycle is a natural ITS ‘probe’ – unristricted and free

5

Centrifugal force

Rider CG

MC CG

Combined CG

Gravity

Our approach and aim

• The aim of the work presented in this paper was to

develop an application software for monitoring the

condition of the road surface via a smartphone app

mounted on a motorcycle.

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Method

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Mobile app Web-base

1) Acquisition of accelerometer sensor data

2) Acquisition of GPS data

3) Design of mobile app interface

1) Data upload and synchronization

from mobile app

2) Interface and output design

Application Development1

Site that consists of:

1) Pothole

2) Hump3) Weaving

4) Abrupt breaking

5) Uneven surface

Site selection2

Type of mounting

1) On the handle bar

2) In the basket

3) In the rider’s backpack

Pilot test3

Amendments to the application4

Motorcyclist test subjects

15 motorcyclists with variation of

1) Gender

2) Type of motorcycle3) Speed

Field test5

Verification and Analysis6

Publication and application of IP7

Development technical specification

8

Bil Technical specification Web system Mobile

1 Scripting language PHP 5.5.34,

HTML,

Cascading Style Sheet (CSS),

JavaScript

HTML,

Cascading Style Sheet (CSS),

JavaScript

2 Web server Apache

3 Framework Code Igniter 3.1.2 Phone Gap / Cordova

4 3rd-party Application Program

Interface (API)

Google Maps

5 Tools / Integrated Development

Environment (IDE)

Xcode 8.1

6 Database MySQL

7 Hardware iPhone 6

Briefing to test rider and crew Test rider going over a pothole

Test rider going over a road hump Test rider performing ‘weaving’ action

Pilot testThe pilot test was conducted in 15th

November 2016 (9.30am) at the

Kajang district.

The objective of the pilot test is to

test ROCOM capability in detecting

the road defects and adverse riding

condition based on the type of

device mounting style.

The mounting style tested for

ROCOM are:

1. On the handle bars,

2. In the motorcycle basket or

compartment

3. In the rider’s backpack

At the starting point Mounting the smartphone

Test rider performing ‘weaving’ action Test rider performing abrupt braking action

The field test was conducted on the

25th November 2016 (9.00am) at

Jalan Seksyen 2/15, Kajang Utama,

Kajang, Selangor.

The objective of the test was to test

ROCOM capabilities obtaining the

acceleration values on each obstacle,

when applied on various type of

riders and type of motorcycles.

Field test

11

A rider at obstacle 6 A rider at obstacle 7

A rider at obstacle 3

The test requires us to test fifteen

(15) qualified riders that had to pass

through a 400m road section which

consists of eight (8) obstacle while

utilizing ROCOM.

Field test

Staring

point

1

23

4

5 6 7

1

2

3

4

5

6

7

8

8

Obstacle 1 – Weaving: Simulate

weaving or lane splitting during

heavy traffic or to simulate an

avoidance manuever.

Obstacle 2, 5 & 7 – Road humps 1, 2

and 3: Simulate passing over traffic

calming devices.

Obstacle 4 – Abrupt braking:

Simulate abrupt braking condition in

an road traffic emergency situation.

Obstacle 3 & 6 – Uneven surface and

pothole: Simulate passing over road

defects such as rutting, corrugation

and pothole.

Field test

Results - Pilot test

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Mounting on the handlebars Placed in the rider’s backpackPlaced inside the basket

The success rate of each

mounting type based on the

each obstacles.

Test track 1: Rider 1 Test track 2: Rider 2

Weaving (n=5) Pothole (n=3) Hump (n=10)Abrupt breaking

(n=5)

On handle bar 50.0% 100.0% 90.0% 50.0%

In the basket 40.0% 100.0% 80.0% 40.0%

In the rider's backpack 20.0% 66.7% 60.0% 0.0%

n = Indicates the number of attempts performed by the rider on each mounting type with respect to each obstacles.

Mounting the

smartphone on the

motorcycle handle bar

with the ROCOM

activated had the

highest success rate

of detecting the

obstacles compared to

the other ways of

mounting the smart

phone

Results - ROCOM• ROCOM is a mobile application and web-based system that is tailored

for motorcycle, in order to analyze the motion of a motorcycle when it interacts with the road surface condition and environment in order to identify the risky road section for motorcycle.

14Mobile AppsWeb-Based System

Conceptual use of ROCOM

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Road shoving or undulating surface or

depression surface such as potholes – resulting

a vertical acceleration

Avoiding Pothole (weaving) – resulting a

horizontal acceleration

Road cracks – resulting a

vertical acceleration

Abrupt stop approaching a hazard –

resulting a vertical or horizontal

acceleration

x/y/z

y/z/x

z/x/y

For ROCOM, we have design it in such a way that the rider would not require the smart phone to be oriented in exact orientation of x, y and z.

ROCOM emphasis on the detection of high or extreme acceleration or g-force with respect to the road defects or anomalies.

extreme acceleration or g-force

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Bil Situation / Condition Result / Limit Value (g and m/s2) Refference

1Minor road crash, high acceleration

or strong vibration

Damage only, shock to

the vehicle

2.0g (19.62 m/s2) White et al. (2011) - Smart Sense

2.5g (24.52 m/s2) eCall - EENA (2014)

2 Roller coaster ride (extreme cases) Unconscious 6g (59 m/s2) McKenney (1970), Glaister (1978)

3 Road crash Fatal 150g (1,471 m/s2) Ernsting and King (1988), Glaister (1978)

4 Sideways accelerations Lowest human

tolerance 11g (108 m/s2) McKenney (1970), Ernsting and King (1988)

5 Forward accelerations Lowest human

tolerance45g (441 m/s2) McKenney (1970), Ernsting and King (1988)

6 Headward acceleration (upward)Lowest human

tolerance25g (245 m/s2) McKenney (1970), Ernsting and King (1988)

How it works

17

Y

ZX

ROCOM calculates the distance traveled, average travel speed and the number of location that has more than 2g.

ROCOM maps out and display the riding path in Google map and mark the road sectionthat are risky to motorcycle.

The motorcycle’s motion (raw data) is collected using the accelerometer built in a smart phone or tablet that records the vertical and horizontal acceleration when it goes over a road defects or adverse traffic condition

Result – Field test

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g-x g-z

g-force

Weaving

Hump 1

Uneven surface 1

Abrupt Breaking

Hump 2

Hump 3Pothole

Maximum threshold of 2g

Sequence number

Uneven surface 2

The acceleration graph for Rider 12 matched with the location of the obstacles

Rider 14

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Results – Field test

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Rider number

Obstacle type 1 2 3 4 5 6 7 8

Average speed

(km/h)Weaving Hump 1

Uneven

surface 1

Abrupt

breakingHump 2

Uneven

surface 2Hump 3 Pothole

Rider 1 14.8 - - 2.71 2.00 - - 2.83* -

Rider 2 14.2 2.94 2.68 2.12 2.39 3.64 3.03 4.63* 2.69

Rider 3 16.4 2.34 - 2.29 2.96 3.70 3.04 3.55* 2.41

Rider 4 16.6 2.16 2.03* 2.63* 2.10* 4.41 3.01 3.01 -

Rider 5 16.1 - 2.83 -2.06* - 2.68 2.07 - 4.30

Rider 6 15.9 - - 2.07 - 2.21 2.00 2.80 -3.26

Rider 7 15.3 2.13 - 2.33 - - - - -

Rider 8 9.3 - - 2.13 - - - - -

Rider 9 12.1 - - - 2.69 2.57 - 3.15 -4.10*

Rider 10 20.5 2.06 2.07 2.01 -2.31* 2.48 2.85 - -

Rider 11 21.0 2.72* 2.02* 2.58* 2.30* 2.81* -2.01** 2.73*

Rider 12 15.8 2.09 2.15 2.31 2.56 5.19* 3.20 2.68 2.44

Rider 13 16.9 - - - - 2.43 - 2.39 -2.00*

Rider 14 12.5 - - 2.21 - 2.25 - - -

Rider 15 20.0 - - - 2.19* 2.05* 2.92* 2.21* -

Average - g 2.35 2.29 1.94 1.87 3.03 2.24 3.00 0.35

Successful detection rate (%) 46.7% 40.0% 80.0% 60.0% 80.0% 60.0% 66.7% 46.7%

Average Successful detection rate (%) 60.0%

( - ): Indicates that there were no acceleration that exceeded 2g,

Rider 11 and 15 used motorcycles with more than 250cc

* indicates that the acceleration occurs in the z-axis, ** indicates that the acceleration occurs in the y-axis

Highest acceleration records with respect to the riders and obstacles

Relationship between rider and detection rate?

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Rider

Successful

detection

rate (%)

Sex Age

Weight

of rider

(kg)

Weight of

motorcycle

(kg)

Total

weight

(kg)

Average

speed

(km/h)

Jenis

Motosikal

(<250cc atau

>250cc)

Jenama motosikal

Pernah

kemalangan

motosikal dalam

tempoh 1 tahun

yang lepas?

Menggunakan

motosikal sebagai

pengangkutan

utama? (Ya /

Tidak)

Purata

perjalanan

dengan

motosikal dalam

sehari (KM)

Ada safety

vest?

(Advantage)

Weaving Hump Braking Potholes

Rider 2 100.0% L 29 70 100 170 14.2 <250cc Yamaha Lagenda 110z Tidak Ya 70 Tiada 2 1 2 4

Rider 12 100.0% P 29 45 90 135 15.8 <250cc ex5 HONDA Tidak Tidak 5 Tiada 3 3 4 3

Rider 3 87.5% L 29 100 100 200 16.4 <250cc Yamaha Lagenda Tidak Tidak 4 Tiada 3 4 4 4

Rider 4 87.5% L 38 80 102 182 16.6 <250cc Modenas GT128 Ya Ya 80 Tiada 5 5 5 3

Rider 11 87.5% L 27 80 139 219 21.0 >250cc KTM Duke Tidak Ya 120 Tiada 2 1 2 5

Rider 10 75.0% L 25 80 90 170 20.5 <250cc ex5 HONDA Tidak Ya 50 Tiada 3 3 3 4

Rider 5 62.5% L 24 80 115 195 16.1 <250cc Yamaha Y15 Tidak Ya 170 Tiada 4 3 4 5

Rider 6 62.5% L 40 80 100 180 15.9 <250cc Yamaha Lagenda 115 Tidak Ya 50 Ada 1 3 2 3

Rider 9 50.0% L 26 80 90 170 12.1 <250cc ex5 HONDA Tidak Ya 50 Tiada 2 3 4 4

Rider 15 50.0% L 37 60 148 208 20.0 >250cc Naza N5 Tidak Ya 200 Tiada 4 3 3 4

Rider 1 37.5% L 46 75 90 165 14.8 <250cc ex5 Honda Tidak Ya 60 Tiada 3 2 4 5

Rider 13 37.5% L 34 85 99 184 16.9 <250cc Suzuki Smash 115 Tidak Tidak 15 Tiada 3 3 3 4

Rider 7 25.0% L 31 80 105 185 15.3 <250cc Yamaha Lc 135 Tidak Ya 100 Tiada 2 2 3 4

Rider 14 25.0% L 34 65 99 164 12.5 <250cc Suzuki Smash 115 Tidak Tidak 100 Tiada 2 1 2 5

Rider 8 12.5% L 23 78 105 183 9.3 <250cc Yamaha Lc 135 Tidak Ya 10 Tiada 3 2 4 4

No pattern or relation due to small sample size

Summary

• Results from the field test indicated that ROCOM is able to detect various obstacles with the average of successful detection rate of 61.9%– The highest successful detection rate (80%) occurs when riders passed

through Uneven surface and Humps 2 and 3.

• ROCOM intends to assist road authorities by providing information on the location of road section that is risky for motorcyclists and plan for road maintenance and road safety audit.

• It is a pro-active measures when it is fully cloud base

• A cost effective measure for detecting road defect

• User friendly and accessible – install in smart mobile devices

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Raw Data

Big data analysis

Government & Road Authorities

ITS Control center & ITS Service Provider

Lab

Road

InfrastructuresMotorcycle manufacturer

Universities

For Safer and Efficient Transportation

Cities and Towns

Improvements and research

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

angu

lar

velo

city

(ra

d/s)

distance(km)

sample1sample2sample3T

HE

FU

TU

RE

Cloud base

Challenges / future plans for ROCOM

• Google map accuracy

– In the process of collaborating with JUPEM to obtain Malaysia base

map

• Public buying in – applying ‘carrot’ to motorcyclists (reducing

cost of bikes or reducing road tax, etc.)

• Video integration is also in the plan – visual tracking

• We have not tested for a long ride – need more capacity?

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Let’s make motorcycles a solution and not the

problem!

25